Automated analysis of and response to social media

ABSTRACT

Methods and systems for managing a public perspective of an entity by analyzing online posts and automatically performing actions in response to these online posts are described herein. Real-time feeds of online posts may be streamed from social media servers. Online posts that are determined to be related to the entity may be extracted from the feeds and accumulated for analysis. A sentiment analysis may be performed on the online posts to determine the public perspective toward the entity. Content of the online posts may be analyzed to determine whether it is related to a prohibited transaction or an inquiry for a product or a service offered by the entity. Actions may be automatically performed to prevent the prohibited transaction from occurring. Resources related to the product or the service may be offered in response to the online post.

RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.15/802,303, filed Nov. 2, 2017, and entitled “Automated Analysis of andResponse to Social Media,” which is incorporated herein by reference inits entirety.

BACKGROUND

The present specification generally relates to management of a publicperspective of an entity, and more specifically to automaticallyperforming actions based on advanced analysis of social media posts tomanage the public perspective of the entity.

RELATED ART

Social media has become a popular outlet for many people to expressthemselves. Typically, users of the social media platforms may use theplatform to express their view points, their attitudes, or theirquestions within their communities on social media. Many of their viewpoints, attitudes, or questions are directed toward a particular brand,an entity behind the particular brand, or a particular product orservice. For example, they may express their opinions about a certainbrand or product (e.g., whether they like it or not, how does theparticular product compared to other similar products, etc.) and/or theymay post their questions or frustrations related to using a certainproduct or service. It has even been observed that people feel morecomfortable about expressing themselves on social media than directlycommunicating with various companies. For example, instead of sending ane-mail or calling a hotline of a company to ask for help, a user of aproduct may opt to post the question regarding the product on a socialmedia platform. Friends of the user within the social media communitymay offer help or may share the same frustration about the product withthe user. Furthermore, potential transactions using a company's productor service that are in violation of the company's policy (and may alsobe illegal) may be advertised on social media. The accumulation of thesesocial media posts related to the product may easily influence thepublic's perspective or perception toward the product and the companythat provides the product.

As such, it is no longer sufficient for companies today to solely relyon using their hotlines, websites, and/or surveys to provide after salesservices to users and capture sentiment from the public. Furthermore,the companies need to take a proactive stance toward managing theirbrands within different social media platforms. Thus, there is a needfor systems and methods that provide analysis of social media in realtime in order to serve users and manage public sentiment of a brand.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 is a block diagram illustrating a social media analysis systemaccording to an embodiment of the present disclosure;

FIG. 2 illustrates analyzing online posts under one approach accordingto an embodiment of the present disclosure;

FIG. 3 illustrates analyzing online posts under another approachaccording to an embodiment of the present disclosure;

FIG. 4 is a flowchart showing a process of analyzing online posts toprevent prohibitive transactions according to an embodiment of thepresent disclosure;

FIG. 5 is a flowchart showing a process of analyzing online posts toprovide automated assistance to users according to an embodiment of thepresent disclosure;

FIG. 6 is a flowchart showing a process of determining a sentiment ofthe public regarding an entity according to an embodiment of the presentdisclosure; and

FIG. 7 is a block diagram of a system for implementing a deviceaccording to an embodiment of the present disclosure.

Embodiments of the present disclosure and their advantages are bestunderstood by referring to the detailed description that follows. Itshould be appreciated that like reference numerals are used to identifylike elements illustrated in one or more of the figures, whereinshowings therein are for purposes of illustrating embodiments of thepresent disclosure and not for purposes of limiting the same.

DETAILED DESCRIPTION

The present disclosure describes methods and systems for managing apublic perspective of an entity by analyzing online posts related to anentity and automatically performing actions in response to these onlineposts. The entity may be a person, a company, or any type oforganization for which someone may submit a public post about. Real-timefeeds may be streamed from one or more social media servers, forexample, by using a streaming application programming interface. Onlineposts that are determined to be related to the entity may be extractedfrom the feeds and accumulated for analysis. Each online post related tothe entity may be analyzed to first determine a category, selected frommultiple categories, associated with the post. The categories mayinclude, but not limited to, a compliance category, an inquiry category,and an opinion category.

In some embodiments, in order to determine a category for the onlinepost, content from the online post may be analyzed. For example, textfrom the online post may be parsed and semantics of the text may beanalyzed to determine whether the online post is related to a question,a potential transaction, or just a statement. In some embodiments, whenthe online post includes image data, image recognition and/or opticalcharacter recognition may be used to extract content from the imagedata. When the online post is determined to be related to a potentialtransaction, the online post may be assigned to the compliance category.When the online post is determined to include a question, the onlinepost may be assigned to the inquiry category. Furthermore, when theonline post is determined to be just a statement about the entity (or aproduct or a service offered by the entity), the online post may beassigned to the opinion category.

Once the online post is categorized, the content of the online post maybe further analyzed to determine an action to be performed in responseto the online post. For example, when the online post is assigned to thecompliance category, the content of the online post may be analyzed todetermine whether the potential transaction discussed in the online postis a transaction that is in violation of the policies of the entity. Akeyword search may be performed on the content (or content extractedfrom the image or video in the online post) to determine if the onlinepost includes one or more keywords that indicate a violation of theentity's policies. If it is determined that the online post is relatedto a potential transaction in violation of the entity's policies, atake-down request of a listing of the item or service associated withthe potential transaction may be automatically generated and transmittedto the social media server from which the online post is retrieved.Furthermore, the online post may be examined to determine a user accountwith the entity (the seller account) associated with the poster of theonline post, based on a user name (handle name) of the poster on thesocial media platform. One or more restrictions may then be applied tothe seller account. For example, the seller account may be deactivatedfor a predetermined duration of time. In another example, a maximumtransaction limit may be placed on the seller account for apredetermined duration of time.

In some embodiments, the online post may also be examined to determinewhether the online post includes a link to a website associated with theentity for facilitating the transaction. For example, the link may bespecifically associated with the seller account in order for the sellerto perform the transaction advertised in the online post. In suchembodiments, a web server of the entity may be automatically configuredto re-direct the link to another website, for example, to a website thatnotifies viewers that the desired transaction is prohibited as it is inviolation of the entity's policies.

In some embodiments, the online post may also be examined to determine auser account with the entity (the buyer account) associated with apotential buyer. For example, users who responded to (or providedcomments to) the online post may be tracked based on their user names(handle names) with the social media platform. When the online postincludes a link to a website associated with the entity, users whoattempt to view the website via the link included in the online post mayalso be tracked. Once a buyer account is identified, one or morerestrictions may be applied to the buyer account. As with the selleraccount, the buyer account may be deactivated for a predeterminedduration of time. In another example, a maximum transaction limit may beplaced on the buyer account for a predetermined duration of time. Insome embodiments, the restrictions placed on the seller account are morerestrictive than the restrictions placed on the buyer account.

When the online post is assigned to the inquiry category, the content ofthe online post may be analyzed to identify a product or a servicerelated to the question in the online post. A keyword search may beperformed to determine whether the online post includes one or morekeywords related to products and/or services offered by the entity. Oncethe product or service is identified, a response may be automaticallygenerated and posted on the social media server in response to theonline post. In some embodiments, the response may include a link to awebsite associated with the product or service (e.g., a frequently askedquestion page for the identified product or service). Furthermore, aninstance of a bot may be instantiated for interacting with this onlinepost (e.g., for communicating with the poster of the online post on thesocial media). The bot may use one or more machine learning algorithmsto understand a context of the online post and any follow-up postsgenerated by the poster, and to generate proper responses to the poster.

In some embodiments, a sentiment analysis may be performed on theoriginal online post and the follow-up posts generated by the poster todetermine a sentiment and/or a sentiment trend of the poster throughoutthe interactions. The sentiment trend may then be used to assist the botin providing the follow-up responses to the posts generated by theposter. For example, if the sentiment trend (e.g., a change of thesentiment) of the poster is trending toward positive, the bot maycontinue to use the same approach in responding to the poster. However,if the sentiment trend of the poster is trending toward negative, thebot may determine to change an approach to respond to the poster. Forexample, the bot may notify a human customer service representative toassist with the poster (e.g., by transmitting a notification to a userdevice associated with a human customer service representative).

In some embodiments, online posts that are assigned to the opinioncategory are accumulated and analyzed. Content of the accumulated onlineposts may be analyzed to determine a common topic of the online posts.For example, the common topic may be related to a particular product orservice offered by the entity. Alternatively, the common topic may bespecifically related to an event associated with the entity, forexample, a recent public announcement made by the entity. Semantics ofthe accumulated online posts that are related to the common topic maythen be analyzed to derive an overall sentiment toward the common topic.In some embodiments, a frequency of the online posts that discuss thecommon topic may be monitored as well. The overall sentiment and thefrequency may then be used to generate a sentiment report associatedwith the common topic.

In some embodiments, a recommendation may be generated based on thederived overall sentiment and the frequency of the online posts. Forexample, when it is determined that the overall sentiment toward arecent publication from the entity is negative, the recommended actionmay include removing the publication from the public domain and/orgenerating a response to the publication. When it is determined that theoverall sentiment toward a recently released new product is positive,the recommended action may include scaling up a production of the newproduct. According to various embodiments of the disclosure, onlineposts that are related to the common topic may continue to be monitored,and a sentiment trend may be generated based on the continuousmonitoring of the online posts. A report indicating the sentiment trend(e.g., a change of the sentiment) related to the common topic may begenerated and presented via a display.

FIG. 1 illustrates a system 100 for analyzing social media feedsaccording to one embodiment of the disclosure. The system 100 includes aservice provider server 102 that is communicatively coupled with varioussocial media servers (such as social media servers 130, 132, and 134)and a user device 120 via a network 160. The network 160, in oneembodiment, may be implemented as a single network or a combination ofmultiple networks. For example, in various embodiments, the network 160may incl4-de the Internet and/or one or more intranets, landlinenetworks, wireless networks, and/or other appropriate types ofcommunication networks. In another example, the network 160 may comprisea wireless telecommunications network (e.g., cellular phone network)adapted to communicate with other communication networks, such as theInternet.

Each of the social media servers 130, 132, and 134 is configured tomaintain a social media platform. Example social media platforms includeTwitter®, Facebook®, Instagram®, and Snapchat®. After a user hasregistered an account with a social media platform, the user may beginto post online posts and receive feeds of online posts generated byother users (e.g., posts generated by friends of the user) through thesocial media platform. As such, each social media server is configuredto maintain feeds of online posts and facilitate generation andpresentation of the online posts. It is noted that while only threesocial media servers are shown in this figure, the service providerserver 102 may be connected with as many (or as few) social mediaservers via the network 160 as desired to perform the functionsdescribed herein.

The service provider server 102, according to some embodiments, may bemaintained by an online service provider, such as PayPal, Inc. of SanJose, Calif., which may provide services related to analyzing onlineposts and generating actions in response to the online posts asdescribed herein. The service provider server 102 may also be configuredto process online financial and information transactions on behalf ofusers. In some embodiments, the service provider server 102 may also beconfigured to provide access to goods and services (collectivelyreferred to as “items”) of a merchant or service provider that are forpurchase and may provide a payment service processing for the purchaseditems. In the examples given here, the service provider server 102 mayperform social media analysis for the online service provider (alsoreferred to as the “entity” hereinafter).

According to various embodiments of the disclosure, the service providerserver 102 may include a streaming application programming interface(API) module 104, a categorization module 106, a compliance module 108,an inquiry module 110, a sentiment analysis module 112, a web server114, an accounts database 116, and a payment application 118. The webserver 114 is configured to serve web content to users in response toHTTP requests. As such, the web server 114 may store pre-generated webcontent ready to be served to users. For example, the web server 114 maystore a log-in page, and is configured to serve the log-in page to usersfor logging into user accounts of the users to access various serviceprovided by the service provider server 102. The web server 134 may alsoinclude other webpages associated with the different services offered bythe service provider server 102, for example a webpage for transferringmoney from one user account to another user account, a webpage forviewing products and services, webpages for describing differentproducts or services offered by the entity, etc. As a result, a user mayaccess a user account associated with the user and access variousservices offered by the service provider server 102, by generating HTTPrequests directed at the service provider server 102.

The accounts database 116 stores information related to user accountsthat were registered with the entity through the service provider server102. The information of a user account stored in the accounts database116 may include a user name, a password (or other types ofauthentication credentials such as fingerprint information), contactinformation, other personal information of the user, transaction historyrelated to the user account, available funds of the user account, andother user account related information. In some embodiments, the paymentapplication 118 may process transactions, such as payment transactions,with a user account based on the account information associated with theuser account stored in the accounts database 116. The service providerserver 102 may include other applications and may also be incommunication with one or more external databases (not shown), that mayprovide additional information to be used by the service provider server102. In some embodiments, the one or more external databases may bedatabases maintained by third parties, and may include third partyaccount information of users who have user accounts with the serviceprovider server 102.

The streaming API module 104 may include application programminginterfaces (APis) corresponding to one or more social media servers(such as social media servers 130, 132, and 134) for streaming real-timesocial media feeds from the social media servers 130, 132, 134. Eachreal-time social media feed may include online posts generated andposted through user accounts with the corresponding social mediaplatform. For example, the streaming API module 104 may stream areal-time social media feed from the social media server 130. Thereal-time social media feed may include online posts generated andposted through user accounts with the social media server 130 in realtime. Real time is defined herein as substantially close to the timethat an event occurs (e.g., within the last five seconds, within thelast 2 seconds, etc.). As such, the streaming API module 104 enables theservice provider server 102 to continuously receive online posts almostimmediately as new online posts are posted on the social media servers130, 132, and 134.

According to various embodiments of the disclosure, each online postfrom the social media feeds is initially examined to determine whetherthe online post is related to the entity. In some embodiments, a keywordanalysis may be performed to determine if the online post includes oneor more keywords related to the entity. The one or more keywords mayinclude a business name of the entity, a name of a product or serviceoffered by the entity, and a slogan associated with the entity. Inaddition, if the online post includes an image, an image recognitionalgorithm may be used to determine if the image includes the one or morekeywords or includes a graphic related to the entity, such as a logo ofthe entity, an image of a product offered by the entity, etc. Onlineposts that are determined to be unrelated to the entity may bediscarded, and online posts that are determined to be related to theentity may be passed to the categorization module 106.

The categorization module I 06 may analyze the content of the onlinepost and assign the online post to one of the many pre-determinedcategories. As discussed above, the pre-determined categories mayinclude a compliance category, an inquiry category, and an opinioncategory. Other categories may also be added to the list, as desired bythe entity. In some embodiments, if it is determined that the onlinepost is related to a potential transaction using a product and/or aservice offered by the entity, the categorization module 106 may assignthe online post to the compliance category. The categorization module106 may determine that the online post is related to a potentialtransaction using one or more methods. In some embodiments, thecategorization module 106 may perform a keyword search in the onlinepost to determine whether the online post includes one or more keywordsrelated to a potential transaction, such as “sale,” “buy,” sell,” “pay,”etc., and may assign the online post to the compliance category when anyone of the keywords is included in the online post. In some embodiments,the categorization module 106 may also determine whether the online postincludes a link to a website associated with the entity for accessing aproduct or a service offered by the entity, and may assign the onlinepost to the compliance category when the online post includes such alink.

FIG. 2 illustrates an example online post 202 retrieved from one of thesocial media servers 130, 132, and 134. As shown, the online post 202includes a user name 204 associated with a user registered with thecorresponding social media server, text data, and an image 206 that isalso a link to a website. Based on a keyword search performed on thetext data in the online post 202, the categorization module 106 maydetermine that keywords related to a potential transaction, such as“sale” and “pay,” appear in the online post. Furthermore, the link 206is directed to a website “www.paypal.com/abc.” By analyzing the link,the categorization module 106 may determine that the link is directed toa website associated with the service provider server 102 for making apayment to a user account registered with the service provider server102. Based on the keyword search performed on the text of the onlinepost and the analysis of the link, the categorization module 106 maydetermine that the online post is related to a potential transactionusing or for one of the products and/or services related to the serviceprovider server 102, and may assign the online post to the compliancecategory.

In some embodiments, the categorization module 106 may analyze thecontent of the online post to determine whether the online post isrelated to an aspect of the entity or to one of the products or servicesoffered by the entity. The categorization module 106 may then assign theonline post to the opinion category when it is determined that theonline post is related to an aspect of the entity or to one of theproducts or services offered by the entity. When the online postincludes image data, the categorization module 106 may also use an imagerecognition algorithm to determine whether the image is related to anaspect of the entity or to one of the products or services offered bythe entity. For example, the categorization module 106 may analyze theimage to determine whether it includes an image of a logo of the entityor an image of a product or service offered by the entity.

FIG. 3 illustrates another example online post 302 retrieved from one ofthe social media servers 130, 132, and 134. As shown, the online post302 includes a user name 304 associated with a user who has beenregistered with the corresponding social media server and text data 306that was generated and posted by the user 304. By performing a keywordsearch on the text data 306, the categorization module 106 may determinethat the text data 306 includes a name of the entity (e.g., “PayPal”)and also a name of the service offered by the entity (“paymentservice”). Based on this determination, the categorization module 106may assign the online post 302 to the opinion category.

In some embodiments, the categorization module 106 may also analyze thesemantics of the content to determine which category is associated withthe online post. For example, if it is determined that the online postincludes a question (e.g., by determining that the online post includesa phrase that ends with a question mark or by determining that a phraseis a question based on a semantic or word analysis of the phrase, suchas a sentence starting with “why,” “what,” or “how”) or whether theonline post indicates a user's frustration with using a product or aservice offered by the entity, the categorization module 106 may assignthe online post to the inquiry category. Referring back to FIG. 3 , thecategorization module 106 may perform a semantic analysis on the textdata 306 and determine that the text data 306 indicates the user beingfrustrated with using one of the services offered by the entity, and maythen assign the online post 306 to the inquiry category. As shown bythis example, it is noted that the categorization module 106 may assignthe same online post to multiple categories.

Based on the category or categories that the online post is assigned to,the categorization module 106 may then pass the online post to one ormore of the analysis engines, including the compliance module 108, theinquiry module 110, and the sentiment analysis module 112. For example,the categorization module 106 may pass online posts that are assigned tothe compliance category to the compliance module 108, may pass onlineposts that are assigned to the inquiry category to the inquiry module110, and pass online posts that are assigned to the opinion category tothe sentiment analysis module 112. As discussed above, the same post maybe passed to more than one analysis module when the post is assigned tomore than one category.

Referring back to FIG. 2 , as discussed above, the online post 202 isassigned to the compliance category, and as such, the categorizationmodule 106 may pass the online post 202 to the compliance module 108.According to various embodiments of the disclosure, the compliancemodule 108 may perform one or more analyses on the online post 202 andmay perform one or more actions in response to the online post 202 basedon the analyses. In some embodiments, the compliance module 108 mayfirst determine whether the potential transaction inferred from theonline post 202 is in violation to policies of the entity. Thecompliance module 108 may perform a keyword search on the text data 204and may also perform image recognition algorithms on any image dataincluded within the online post 202. For example, if it is in violationof the entity's policy to use a service provided by the entity in atransaction that involves, pharmaceutical or illicit drugs, pornography,and electronic cigarettes, the compliance module 108 may determinewhether the text data 204 includes one or more of the keywords “drug,”“pharma,” “pharmaceutical,” “porn,” “nude,” “e-cig,” etc., and determinewhether the image data included in the online post 202 is related to anyone of these products. In this example, the compliance module 108 mayconclude that the online post 202 is related to a potential transactionfor selling drugs, which is in violation to the entity's policies, basedon the presence of the words “drugs” and “sale” within the same phrasein the text of the online post 202.

Once it is determined that the online post 202 is related to a potentialtransaction in violation of the entity's policies, the compliance module108 of some embodiments may determine a user account with the serviceprovider server 102 (the seller account) that is used for selling drugson the social media. In some embodiments, the compliance module 108 maydetermine the seller account based on information related to the useraccount 204 with the social media server (e.g., the social media server130). It is noted that a user may likely use identical (or very similar)user names across multiple platforms and entities, and so the user name(or handle name) being used with the social media server 130 may beidentical or substantially similar to a user name being used with theseller account. Thus, the compliance module 108 may search through theaccounts database 116 to identify a user account with the serviceprovider server 102 having a user name identical or substantiallysimilar to the user name (also referred to as “handle name” herein) 204‘JasonM34.’ In some embodiments, the compliance module 108 may alsoscrap online posts generated by the user account 204 ‘JasonM34’ on thesocial media server 130 to extract additional information, such asresidence location of the user, contact information of the user,purchase history of the user, locations that the user has recentlyvisited, etc., to assist the user account risk assessment module 208 toidentify the seller account with the service provider server 102 thatcorresponds to the user account 204 ‘JasonM34’ with the social mediaserver 130.

As shown in FIG. 2 , in addition to the text, the online post 202 alsoincludes a link 206 for directing viewers to a website 210. In thisexample, the link 206 includes a web address 208 ‘www.paypal.com/abc.’In some embodiments, the compliance module 108 may examine the webaddress 208 from the link 206, and may determine that the web address208 is associated with the entity and the service provider server 102based on the domain name appears in the web address 208. By examiningthe web address and/or examining the content within the website 210, thecompliance module 108 may also determine that the web address points tothe website 210 for users to make payments to a user account having ahandle name ‘Jmorales34’ with the service provider server 102. In thisregard, the compliance module 108 may transmit the web address 208 tothe web server 114 to pull information related to the website 210 fromthe web server 114. While the user name ‘Jmorales34’ is not identical tothe user name 204 ‘JasonM34,’ the compliance module 108 may stillconclude that the user name ‘Jmorales34’ is associated with the selleraccount used to sell drugs via the online post 202 based on theconnection between the user name ‘Jmorales34’ and the online post 202,and the similarity between the user names ‘Jmorales34’ and ‘JasonM34.’

After identifying the seller account, the compliance module 108 mayimplement additional restrictions on the seller account to prevent thepotential transaction from occurring. For example, the compliance module108 may deactivate the seller account for a predetermined duration oftime, denying any transaction request (e.g., a payment transactionrequest) generated by the seller account, and/or impose a maximumtransaction limit on the seller account for a predetermined duration oftime. This way, the seller associated with the user name ‘JasonM34’would not be able to make inappropriate transactions using the serviceoffered by the entity for at least a duration of time. The compliancemodule 108 may also transmit a notification to the seller account basedon contact information listed in the accounts database 116 associatedwith the seller account, notifying the seller of his or her violation ofthe entity's policies.

In addition to identifying the seller account, the compliance module 108of some embodiments may also identify one or more buyer accounts withthe service provider server 102 that are associated with potentialbuyers in the potential transaction. For example, when it is detectedthat one or more users of the social media server 130 responds to theonline post 202 (for example, based on the social media feedscontinuously retrieved from the social medial server 130), thecompliance module 108 may use information related to those users of thesocial media server 130 (e.g., using the handle name of the posters whoresponded to the online post 202, etc.) to identify one or more buyeraccounts with the service provider server 102, in a similar manner asdescribed above in identifying the seller account. Once the buyeraccounts are identified, the compliance module 108 may also imposerestrictions on the buyer accounts, such as deactivating the buyeraccounts for a predetermined duration of time or imposing a maximumtransaction limit for a predetermined duration of time. In someembodiments, the restrictions imposed on the buyer accounts are lessrestrictive than the restrictions imposed on the seller account. Forexample, only a maximum transaction amount is imposed on the buyeraccounts while the seller account is deactivated, or the duration of therestriction is shorter for the buyer accounts than the duration of theseller account.

In addition to implementing restrictions on the seller accounts and/orthe buyer accounts, the compliance module 108 of some embodiments mayalso work with the web server 114 to prevent the potential transactionbased on the online post 202. In some embodiments, after receiving thelink 206 from the compliance module 108, the web server 114 may beconfigured to redirect any HTTP requests for the web address 208 toanother website 212 different from the website 210 so that other userswho intended to complete the potential transaction based on the onlinepost 202 by clicking on the link 206 would be prevented from completingthe transaction. The redirected website 212 may include a messageindicating that the transaction that the user wants to complete is beingdenied. After the reconfiguration of the web server 114, a user makingan HTTP request based on the web address 208, for example by clicking onthe link 206 from the user computer 120, would be redirected to thewebsite 212. Since the service provider server 102 may retrieve feeds ofonline posts in real-time, the service provider server 102 mayimmediately respond to posts that may trigger potential transactionsthat are in violation of the entity's policies, and may effectivelyprevent the potential transactions from occurring.

Furthermore, the compliance module 108 may also send the post 202 andadditional information derived from the post 202, as discussed above, toa database within the service provider server 102 such that another teamwithin the entity may examine the post 202 further.

Referring to FIG. 3 , as discussed above, the online post 302 isassigned to the inquiry category, and as such, the categorization module106 may pass the online post 302 to the inquiry module 110. According tovarious embodiments of the disclosure, the inquiry module 110 mayperform one or more analyses on the online post 302 and may perform oneor more actions in response to the online post 302 based on theanalyses. As shown in FIG. 3 , the online post 302 includes a user name304 ‘JasonM34’ associated with a user of the social media server (e.g.,the social media server 130) who generated the posted 302. The onlinepost 302 also includes content 306 generated by the user account 304. Insome embodiments, the compliance module 108 may first determine aproduct or a service that the user 304 is having a problem with, byanalyzing the content 306. The inquiry module 110 may perform a keywordsearch on the content 306 when the content includes text data, and mayalso perform image recognition algorithms on any image data includedwithin the content 306, to determine whether the content 306 indicates aproduct or a service offered by the entity that the user seems to have aproblem with. In this example, by performing a keyword search, theinquiry module 110 may determine that the content 306 of the online post302 includes the name of the entity ‘PayPal’ and also the name of aservice ‘Payment Service’ offered by PayPal. As such, the inquiry module110 may conclude that the online post 302 is related to the service‘Payment Service,’ and retrieve, from the web server 114 resourcesrelated to the ‘Payment Service.” For example, the inquiry module 110may retrieve from the web server 114 a web address for a website thataddresses the ‘Payment Service’ (e.g., a frequently asked questionswebsite for the Payment Service such as http;//www.paypal.com/help/payservices). In some embodiments, a user account with each of the varioussocial media servers 130, 132, and 134 may be established for theentity. The inquiry module 110 may then generate a response to the post302 by including the web address in the response, and post the responseto the social media server 130 under the user account of the entity.FIG. 3 illustrates a response 308 that includes the web address beingposted on the social media server 130 as a response to the online post302 generated by the user 304.

According to various embodiments of the disclosure, in addition toproviding the initial response to the online post 302, the inquirymodule 110 may continue to monitor activities related to the online post302, and may provide additional follow-up responses based on themonitored activities. In some embodiments, the inquiry module 110 mayinstantiate a new instance of a bot for each of the online posts itneeds to monitor. The bot may function as an instance of the inquirymodule 110 but provide functionalities only with respect to a specificonline post. For example, when it is detected that the user 304, viauser computer 120, generated a follow-up post (may be in response to theresponse 308 generated by the inquiry module 110) related to the onlinepost 304, the bot may analyze the follow-up post by the user 304 andgenerate follow-up responses to assist the user 304.

For example, the user 304 may generate a follow-up post, in response tothe response 308, by further explaining the user's problems with usingthe Payment Service, the solution of which is not found in thefrequently asked question section of the website. Thus, the bot mayprovide additional information in an attempt to solve the problems ofthe user 304. In this regard, the bot (an instance of the inquiry module110) may use one or more machine learning algorithms to provide contextwithin the conversation between the user 304 and the bot. The machinelearning algorithms may help the bot to analyze the follow-up post(s)generated by the user 304 to understand if the user wants information onsomething more specific within the service, wants information onsomething broader within the service, or wants information about adifferent aspect of the service. The bot may then use the contextderived from the follow-up posts to query the web server 114 fordifferent resources to provide to the user 304. Again, the bot maygenerate additional responses that include the resources retrieved fromthe web server 114, and post the additional responses to the socialmedia server 130.

In some embodiments, the inquiry module 110 may send information relatedto the post 302 to the sentiment analysis module 112 to derive asentiment of the post and/or a sentiment trend of the post and thefollow-up posts generated by the user 304. By determining a sentiment ofthe user 304, the inquiry module 110 may provide improvements in theresponses by modifying the response (e.g., providing additional words orphrases) based on the sentiment of the user 304. For example, if thesentiment analysis module 112 determines that the user 304 isfrustrated, the inquiry module 110 (or the bot) may add phrases such as“we are sorry that you are frustrated,” “thank you for being patient,”etc. when generating the responses to the post 302. In addition, as theinquiry module 110 continues to feed new data related to the online post302 (e.g., new follow-up posts from the user 304) to the sentimentanalysis module 112, the sentiment analysis module 112 may alsodetermine a sentiment trend of the user 304 over the conversation withthe inquiry module 110. As such, the inquiry module 110 may also modifya response to the follow-up post based on the determined sentimenttrend. For example, when it is determined that the sentiment of the user304 is trending downward (e.g., getting more and more frustrated), theinquiry module 110 may transmit a notification to a user device 310associated with a human customer service representative 312 with theentity. The notification may include a link to the online post 302 andinformation derived from the online post 302 previously by the inquirymodule and the sentimental analysis module 112 (e.g., the sentiment andsentiment trend of the user 304, information related to the user 304,etc.) so that the human customer representative 312 may use the userdevice 310 to further communicate with the user 304, possibly via theonline post 302. The techniques in determining a sentiment and asentiment trend based on information of a post are described in moredetailed below.

Furthermore, the inquiry module 110 may also send the post 302 andadditional information derived from the post 302, as discussed above, toa database within the service provider server 102 such that another teamwithin the entity may examine the post 302 further.

As discussed above, in addition to being assigned to the inquirycategory, the online post 302 is also assigned to the opinion category,and as such, the categorization module 106 may also pass the online post302 to the sentiment analysis module 112. According to variousembodiments of the disclosure, the sentiment analysis module 112 mayperform one or more analyses on the online post 302 and may perform oneor more actions in response to the online post 302 based on theanalyses. In some embodiments, the sentiment analysis module 112 mayaccumulate multiple online posts that are assigned to the opinioncategory, for example, accumulating online posts that are assigned tothe opinion category over a period of time (e.g., several hours, weeks,months, etc.). In some of these embodiments, the sentiment analysismodule 112 may examine the each of the accumulated online posts toextract a related topic for the online post (for example, based on akeyword search in the content of the online post), and may then furthercategorize the online posts based on the extracted topic. In someembodiments, the sentiment analysis module 112 may determine that theonline posts are related to a topic when a frequency of one or morekeywords related to the topic appear in the online posts exceeds apredetermined threshold.

For example, the sentiment analysis module 112 may determine a firstgroup of online posts that are related a new product being releasedrecently by the entity, as the examination of the content of the onlineposts indicate that the online posts within this group includeexpressions of opinions (e.g., like, dislike, etc.) regarding the newproduct (e.g., the online posts in this group include the name of thenew product). The sentiment analysis module 112 may also determine asecond group of online posts that are related to a service that has beenoffered by the entity, as the examination of the content of these onlineposts indicate that the online posts within this group includeexpressions of opinions regarding the service (e.g., the online posts inthis group include the name of the service).

In some embodiments, the sentiment analysis module 112 may then analyzethe online posts in each group to derive an overall sentiment of thepublic regarding the topic extracted from the online posts in thisgroup. When analyzing an online post, the sentiment analysis module 112may determine a sentiment of the post based on word(s) being used in thepost. For example, the sentiment of a post is determined to be positiveif the post includes positive words such as “like,” “fond of,”“awesome,” “great,” etc. and the sentiment of the post is determined tobe negative if the post includes negative words such as “dislike,”“hate,” “sucks,” etc. In addition, the sentiment analysis module 112 maydetermine a sentiment of the post based on the use of punctuation marksand/or capitalization. It can be observed that the amount of certainpunctuation marks such as ‘!’ and ‘?’ and the capitalization of wordsindicates a heightened intensity of a sentiment. As such, if it isdetermined that the online post includes a positive word with a lot ofexclamation marks, the sentiment analysis module 112 may determine ahigh degree of positive sentiment for the post, (a higher degree thanthat of another post with positive words without exclamation marks).Similarly, if it is determined that the online post includes a negativeword with a lot of exclamation marks, the sentiment analysis module 112may determine a high degree of negative sentiment for the post, (ahigher degree than that of another post with negative words withoutexclamation marks).

In one embodiment, semantic or word analysis of the online post is basedon the user who made the post. For example, the user may typically postin all capital letters, use exclamation points, and/or use certainexpressive language. In that case, presence of such content may beweighed or treated differently than another user who does not normallyuse such punctuation or language in online posts. This then may resultin a different sentiment between users, even though the punctuationand/or language of the posts are similar, where the post from the firstuser may be determined as neutral, while the post from the second usermay be determined as angry or negative. By adjusting the analysis basedon the specific user, more accurate sentiments may be obtained for bothindividuals and groups.

By analyzing each of the online posts in the first group, the sentimentanalysis module 112 may determine an overall sentiment for the topicrelated to the first group. For example, the sentiment analysis module112 may determine an average sentiment based on the sentimentsdetermined for the online posts in the first group. In another example,the sentiment analysis module 112 may generate statistics based on thedetermined sentiments of the online posts in the first group, such as apercentage of online posts that are positive regarding the new product,a percentage of online posts that are negative regarding the newproduct, etc. In some embodiments, the sentiment analysis module 112 mayalso capture a frequency of online posts that are related to the topicto determine the overall sentiment for the group. For example, thesentiment analysis module 112 may increase an intensity of the overallsentiment when a frequency of online posts is above a predeterminedthreshold, and may reduce the intensity of the overall sentiment whenthe frequency is below the predetermined threshold.

The sentiment analysis module 112 may also determine a cause to thedetermined overall sentiment. By analyzing the content of the posts thatindicate a negative sentiment of the product, the sentiment analysismodule 112 may determine that many users are frustrated with aparticular feature of the product. The sentiment analysis module 112 maythen retrieve online resources related to that particular feature (e.g.,from the web serve 114) and may automatically respond to the onlineposts with a link to a retrieved resource (e.g., a website comprising auser manual for the particular feature).

In some embodiments, the sentiment analysis module 112 may continue toaccumulate online posts from the social media servers 130, 132, and 134for the different extracted topics, and generate a sentiment trend foreach of the topic. For example, the first group of online posts relatedto the new product may be accumulated during a first period of time. Thesentiment analysis module 112 may then accumulate new online postsrelated to the new product generated during a second period of timesubsequent to the first period of time. The sentiment analysis module112 may then determine an overall sentiment for the online postsaccumulated in each of the two periods, and then derive a sentimenttrend based on the overall sentiments determined for the two periods.The sentiment analysis module 112 may continue to accumulate more onlineposts related to the new product in subsequent periods of time to add tothe sentiment trend. For example, if the overall sentiment determinedfor the first period is slightly negative and the overall sentimentdetermined for the second period is slightly positive, the sentimentanalysis module 112 may determine that the overall sentiment is trendingupward.

The sentiment analysis module 112 may then store the sentimentinformation in a database, and may present the sentiment information ina report for each topic. For example, the sentiment analysis module 112may generate a report that indicates the overall sentiment informationfor online posts related to the new product and may automaticallytransmit the report to the product manager. The report may include theoverall sentiment of the online posts related to the new product, thesentiment trend (e.g., a change of the sentiment over periods of time),and other sentiment information derived by the sentiment analysis module112.

In some embodiments, the sentiment analysis module 112 may also trackany events related to the topic that occurred when the overall sentimentfor the topic changes. For example, the sentiment analysis module 112may identify a release of a new version of the product between the firstperiod of the time and the second period of time. The sentiment analysismodule 112 may then correlate the sentiment shift (trend) to the eventof releasing the new version. In another example, when the sentimentanalysis module 112 determines that the online posts having negativesentiment are related to a recent publication published by the entity,the sentiment analysis module 12 may automatically remove thepublication from the Internet, for example, by removing the webpage thatincludes the publication from the web server 114. The sentiment analysismodule 112 may also generate a follow-up publication (e.g., a pressrelease) based on the publication.

This information may also be included in the report presented to themanager. As a result of correlating the upward sentiment trend to therelease of the new version of the product, the sentiment analysis modulemay automatically release a public announcement of the new version ofthe product to promote the new version, for example, by generating anonline post and uploading the online post to the various social mediaservers 130, 132, and 134 under the entity's accounts. The sentimentanalysis module 112 may also instruct a manufacturing facility of theentity to scale up a production of the new version of the product. Thesentiment analysis module 112 may also automatically respond to theaccumulated posts determined to have a negative sentiment toward theproduct with the online post including information about the new versionof the product.

In some embodiments, the extracted topic of a group of online posts maynot be related to a specific product or a specific service offered bythe entity, but instead, related to a campaign associated with theentity. For example, the group of online posts may be related to acampaign to boycott the entity. The sentiment analysis module 112 maydetermine a frequency of such online posts and may automaticallytransmit an alert to a user device of a manager of the entity when thefrequency of the posts related to the campaign exceeds a predeterminedthreshold.

Furthermore, the sentiment analysis module 112 may also send the post302 and additional information derived from the post 302, as discussedabove, to a database within the service provider server 102 such thatanother team within the entity may examine the post 302 further.

FIG. 4 illustrates a process 400 for analyzing online posts to preventtransactions that violate an entity's policy according to an embodimentof the disclosure. In some embodiments, the process 400 may be performedby the compliance module 108. The process 400 begins with retrieving (atstep 405) streams of online posts from different servers. For example,the service provider server 102 may use the streaming API module 104 toretrieve streams of online posts from the social media servers 130, 132,and 134. In another embodiment, content from the posts may be scraped,in real-time, by the streaming API module 104. As used herein,retrieving can include scraping content from the online posts.

The process 400 then determines (at step 410) that a first post from thestreams of online posts includes content related to a potentialtransaction that is prohibited by the entity. For example, thecompliance module 108 may perform a keyword search and/or an imagerecognition algorithm on the content of the online post to determinewhether the online post is related to a potential transaction that is inviolation of the entity's policy. When it is determined that the firstpost is related to a prohibited transaction, the process 400 identifies(at step 415) a seller account with the service provider server 102corresponding to a seller in the potential transaction. For example, thecompliance module 108 may analyze the user name of the poster of thefirst online post to determine the seller account with the serviceprovider server 102. The compliance module 108 may also scrape socialmedia data associated with the poster to retrieve additional informationof the poster, such as a residence address, contact information, apurchase history etc., and may use this information to identify theseller account within the accounts database 116. In some embodiments,when the first post includes a link to a website associated with theservice provider server 102 (e.g., a webpage for facilitating a paymentto the seller account), the compliance module 108 may also analyze thelink to determine whether the link indicates a user account with theservice provider server 102, and may conclude that the user account isthe seller account of the seller in the potential transaction.

Once the seller account is identified, the process 400 may apply (atstep 420) one or more restrictions on the seller account. For example,the compliance module 108 may deactivate the seller account for apredetermined period of time or impose a maximum transaction limit onthe seller account for a predetermined period of time, such that theseller is no longer able to use the seller account to complete thepotential transaction. In addition, the compliance module 108 may alsoconfigure the web server 114 to redirect the link to another website ofthe entity such that potential buyers who wish to complete a transactionwith the poster of the first online post may no longer use the link tocomplete the transaction.

FIG. 5 illustrates a process 500 for analyzing online posts to provideautomated assistance to users according to an embodiment of thedisclosure. In some embodiments, the process 500 may be performed by theinquiry module 110 and the sentiment analysis module 112. The process500 begins with retrieving (at step 505) streams of online posts fromdifferent servers. For example, the service provider server 102 may usethe streaming API module 104 to retrieve streams of online posts fromthe social media servers 130, 132, and 134. In another embodiment, thestreaming API module 104 scrapes content from the online posts in realtime, thereby eliminating the need to retrieve the online posts. Theprocess 500 then determines (at step 510) that a first post from thestreams of online posts includes content related to a product or aservice offered by the entity. For example, the inquiry module 110 mayperform a keyword search and/or an image recognition algorithm on thecontent of the online post to determine whether the online post isrelated to a particular product or a service offered by the entity.

Once it is determined that the first online post is related to a productor a service of the entity, the process 500 determines (at step 515) asentiment of the first post. For example, the sentiment analysis module112 may analyze the semantics (words, punctuations, phrases, etc.) ofthe first online post to determine a sentiment for the first post. Thedetermined sentiment may indicate a positive or a negative sentiment. Insome embodiments, the determined sentiment may also include an intensityof the sentiment.

The process 500 then generates (at step 520) a response to the firstpost based on the related product or service and the determinedsentiment of the first post. For example, the inquiry module 110 mayretrieve an online resource related to the product or service indicatedin the first post (e.g., a frequently asked question webpage for theproduct or service) and may include a link to the online resource in theresponse. Based on the sentiment of the first post, the sentimentanalysis module 112 may modify the response, for example, by addingphrases such as “thank you for your patience” to the response. Theprocess 500 then posts (at step 525) the response with the server fromwhich the first online post was retrieved.

FIG. 6 illustrates a process 600 for determining a sentiment of thepublic regarding an entity by analyzing online posts according to anembodiment of the disclosure. In some embodiments, the process 600 maybe performed by the sentiment analysis module 112. The process 600begins with obtaining (at step 505) or scrape content from online postfeeds from different servers. For example, the service provider server102 may use the streaming API module 104 to obtain or scrape contentfrom online post feeds from the social media servers 130, 132, and 134.The process 600 then aggregates (at step 610), from the feeds, onlineposts or content that are related to an entity or an aspect of theentity. For example, the sentiment analysis module 112 may performkeyword search and/or image recognition algorithm on the content of theonline posts to determine if the online post is related to the entity ora product or a service offered by the entity. As discussed above, thesentiment analysis module 112 may then aggregate the online posts thatare related to the same entity (or same product or service) into agroup.

The process 600 then determines (at step 615) an overall sentiment ofthe aggregated posts. For example, the sentiment analysis module 112 mayanalyze the semantics (e.g., choice of words, choice of punctuations,etc.) of each online post to determine a sentiment for the online post.The sentiment analysis module 112 may then determine an overallsentiment of the aggregated posts based on the sentiments determined foreach post in the group. In some embodiments, the sentiment analysismodule 112 may use an average sentiment computed based on all thesentiments determined for the aggregated posts as the overall sentiment.

At step 620, the process 600 extracts a common topic across theaggregated posts. For example, the sentiment analysis module 112 maydetermine that most of the posts (e.g., over 50% or some otherthreshold) having negative sentiment toward the product discuss aspecific feature of the product. The process 600 then correlates (atstep 625) a recent activity from the entity to the overall sentimentbased on the extracted topic. For example, the sentiment analysis module112 may identify a release of a new version of the product that adds thespecific feature to the product prior to the aggregated posts beingobtained. The sentiment analysis module 112 may then correlate therelease of the new version to the overall negative sentiment toward theproduct.

The process then recommends (at step 630) an action based on the overallsentiment and the correlated recent activity. For example, aftercorrelating the release of the new version to the overall negativesentiment toward the product, the sentiment analysis module 112 mayrecommend a release of a newer version to improve that specific featureof the product. In some embodiments, the sentiment analysis module 112may also automatically retrieve an article describing the feature andhow to use the feature (e.g., from the web server 114), andautomatically post the article on the social media servers 130, 132, and134. In addition, the sentiment analysis module 112 may also respond tothe negative posts about the feature with the article as well.

FIG. 7 is a block diagram of a computer system 700 suitable forimplementing one or more embodiments of the present disclosure,including the service provider server 102, the social media servers 130,132, and 134, and the user device 120. In various implementations, theuser device 120 may include a mobile cellular phone, personal computer(PC), laptop, wearable computing device, etc. adapted for wirelesscommunication, and each of the service provider server 102 and thesocial media servers 130, 132, and 134 may include a network computingdevice, such as a server. Thus, it should be appreciated that thedevices 102, 130, 132, 134, and 102 may be implemented as computersystem 700 in a manner as follows.

Computer system 700 includes a bus 712 or other communication mechanismfor communicating information data, signals, and information betweenvarious components of computer system 700. Components include aninput/output (I/O) component 704 that processes a user (i.e., sender,recipient, service provider) action, such as selecting keys from akeypad/keyboard, selecting one or more buttons or links, etc., and sendsa corresponding signal to bus 712. I/O component 704 may also include anoutput component, such as a display 702 and a cursor control 708 (suchas a keyboard, keypad, mouse, etc.). The display 702 may be configuredto present a login page for logging into a user account or a checkoutpage for purchasing an item from a merchant. An optional audioinput/output component 706 may also be included to allow a user to usevoice for inputting information by converting audio signals. Audio I/Ocomponent 706 may allow the user to hear audio. A transceiver or networkinterface 720 transmits and receives signals between computer system 700and other devices, such as another user device, a social media server,or a service provider server via network 722. In one embodiment, thetransmission is wireless, although other transmission mediums andmethods may also be suitable. A processor 714, which can be amicro-controller, digital signal processor (DSP), or other processingcomponent, processes these various signals, such as for display oncomputer system 700 or transmission to other devices via a communicationlink 724. Processor 714 may also control transmission of information,such as cookies or IP addresses, to other devices.

Components of computer system 700 also include a system memory component710 (e.g., RAM), a static storage component 716 (e.g., ROM), and/or adisk drive 718 (e.g., a solid state drive, a hard drive). Computersystem 700 performs specific operations by processor 714 and othercomponents by executing one or more sequences of instructions containedin system memory component 710. For example, processor 714 can performthe functionalities described herein according to the processes 400,500, and 600.

Logic may be encoded in a computer readable medium, which may refer toany medium that participates in providing instructions to processor 714for execution. Such a medium may take many forms, including but notlimited to, non-volatile media, volatile media, and transmission media.In various implementations, non-volatile media includes optical ormagnetic disks, volatile media includes dynamic memory, such as systemmemory component 710, and transmission media includes coaxial cables,copper wire, and fiber optics, including wires that comprise bus 712. Inone embodiment, the logic is encoded in non-transitory computer readablemedium. In one example, transmission media may take the form of acousticor light waves, such as those generated during radio wave, optical, andinfrared data communications.

Some common forms of computer readable media includes, for example,floppy disk, flexible disk, hard disk, magnetic tape, any other magneticmedium, CD-ROM, any other optical medium, punch cards, paper tape, anyother physical medium with patterns of holes, RAM, PROM, EPROM,FLASH-EPROM, any other memory chip or cartridge, or any other mediumfrom which a computer is adapted to read.

In various embodiments of the present disclosure, execution ofinstruction sequences to practice the present disclosure may beperformed by computer system 700. In various other embodiments of thepresent disclosure, a plurality of computer systems 700 coupled bycommunication link 724 to the network (e.g., such as a LAN, WLAN, PTSN,and/or various other wired or wireless networks, includingtelecommunications, mobile, and cellular phone networks) may performinstruction sequences to practice the present disclosure in coordinationwith one another.

Where applicable, various embodiments provided by the present disclosuremay be implemented using hardware, software, or combinations of hardwareand software. Also, where applicable, the various hardware componentsand/or software components set forth herein may be combined intocomposite components comprising software, hardware, and/or both withoutdeparting from. the spirit of the present disclosure. Where applicable,the various hardware components and/or software components set forthherein may be separated into sub-components comprising software,hardware, or both without departing from. the scope of the presentdisclosure. In addition, where applicable, it is contemplated thatsoftware components may be implemented as hardware components andvice-versa.

Software in accordance with the present disclosure, such as program.code and/or data, may be stored on one or more computer readablemediums. It is also contemplated that software identified herein may beimplemented using one or more general purpose or specific purposecomputers and/or computer systems, networked and/or otherwise. Whereapplicable, the ordering of various steps described herein may bechanged, combined into composite steps, and/or separated into sub-stepsto provide features described herein.

The various features and steps described herein may be implemented assystems comprising one or more memories storing various informationdescribed herein and one or more processors coupled to the one or morememories and a network, wherein the one or more processors are operableto perform steps as described herein, as non-transitory machine-readablemedium comprising a plurality of machine-readable instructions which,when executed by one or more processors, are adapted to cause the one ormore processors to perform a method comprising steps described herein,and methods performed by one or more devices, such as a hardwareprocessor, user device, server, and other devices described herein.

What is claimed is:
 1. A system associated with a service provider, thesystem comprising: a non-transitory memory; one or more hardwareprocessors in coupled with the non-transitory memory and configured toread instructions from the non-transitory memory to cause the system toperform operations comprising: retrieving, from a social media servervia a network, an online post generated by a first social media accountwith the social media server; analyzing content of the online post;determining, based on the analyzing, that the online post is associatedwith an offer for sale of a first item determined to be prohibited bythe service provider; identifying a link in the online post for payingfor the first item via the service provider, wherein the link isdirected to a first webpage of the service provider for providing apayment to a first user account; causing a web server of the serviceprovider to redirect requests for the first webpage to a second webpageof the service provider; and in response to receiving a paymenttransaction request for a purchase associated with the first useraccount, denying the payment transaction request.
 2. The system of claim1, wherein the online post comprises text data, and wherein theanalyzing the content of the online post comprises performing a keywordanalysis on the text data of the online post.
 3. The system of claim 1,wherein the online post comprises image data, and wherein the analyzingthe content of the online post comprises performing an image recognitionalgorithm on the image data of the online post.
 4. The system of claim1, wherein the operations further comprise extracting information fromthe first social media account.
 5. The system of claim 4, wherein theextracting the information comprises: extracting a handle nameassociated with the first social media account; and searching through aplurality of user accounts with the service provider to determine thefirst user account corresponding to the first social media account basedon the handle name.
 6. The system of claim 4, wherein the extracting theinformation comprises: scraping the online post and other social mediaonline posts generated through the first social media account toidentify contact information associated with the first social mediaaccount; and searching through a plurality of user accounts with theservice provider to determine the first user account corresponding tothe first social media account based on the contact information.
 7. Thesystem of claim 1, wherein the operations further comprise applying oneor more restrictions to the first user account.
 8. A method comprising:retrieving, by one or more hardware processors associated with a serviceprovider, an online post from a social media server via a network, theonline post being generated by a first social media account with thesocial media server; determining, by the one or more hardware processorsand based on analyzing the online post, that the online post isassociated with an offer for sale of a first item determined to beprohibited by the service provider; identifying, by the one or morehardware processors, a link in the online post for paying for the firstitem via the service provider, wherein the link is directed to a firstwebpage of the service provider for providing a payment to a first useraccount; causing, by the one or more hardware processors, a web serverof the service provider to redirect requests for the first webpage to asecond webpage of the service provider; and in response to receiving apayment transaction request for a purchase associated with the firstuser account, denying the payment transaction request.
 9. The method ofclaim 8, wherein the online post comprises text data, and wherein theanalyzing the online post comprises performing a keyword analysis on thetext data of the online post.
 10. The method of claim 8, wherein theonline post comprises image data, and wherein the analyzing the onlinepost comprises performing an image recognition algorithm on the imagedata of the online post.
 11. The method of claim 8, further comprising:extracting information from the first social media account.
 12. Themethod of claim 11, wherein the extracting the information comprises:extracting a handle name associated with the first social media account;and searching through a plurality of user accounts with the serviceprovider to determine the first user account corresponding to the firstsocial media account based on the handle name.
 13. The method of claim11, wherein the extracting the information comprises: scraping theonline post and other social media online posts generated through thefirst social media account to identify contact information associatedwith the first social media account; and searching through a pluralityof user accounts with the service provider to determine the first useraccount corresponding to the first social media account based on thecontact information.
 14. The method of claim 8, further comprisingapplying one or more restrictions to the first user account.
 15. Anon-transitory machine-readable medium having stored thereonmachine-readable instructions executable to cause a machine to performoperations comprising: retrieving, from a social media server via anetwork, an online post generated by a first social media account withthe social media server; analyzing the online post; determining, basedon the analyzing, that the online post is associated with an offer forsale of a first item determined to be prohibited by a service provider;identifying a link in the online post for paying for the first item viathe service provider, wherein the link is directed to a first webpage ofthe service provider for providing a payment to a first user account;causing a web server of the service provider requests for the firstwebpage to a second webpage of the service provider; and in response toreceiving a payment transaction request for a purchase associated withthe first user account, denying the payment transaction request.
 16. Thenon-transitory machine-readable medium of claim 15, wherein the onlinepost comprises text data, and wherein the analyzing the online postcomprises performing a keyword analysis on the text data of the onlinepost.
 17. The non-transitory machine-readable medium of claim 15,wherein the online post comprises image data, and wherein the analyzingthe online post comprises performing an image recognition algorithm onthe image data of the online post.
 18. The non-transitorymachine-readable medium of claim 15, wherein the operations furthercomprise extracting information from the first social media account. 19.The non-transitory machine-readable medium of claim 18, wherein theextracting the information comprises: extracting a handle nameassociated with the first social media account; and searching through aplurality of user accounts with the service provider to determine thefirst user account corresponding to the first social media account basedon the handle name.
 20. The non-transitory machine-readable medium ofclaim 18, wherein the extracting the information comprises: scraping theonline post and other social media online posts generated through thefirst social media account to identify contact information associatedwith the first social media account; and searching through a pluralityof user accounts with the service provider to determine the first useraccount corresponding to the first social media account based on thecontact information.